Thanks for sharing! Very interesting point of view. My only comment is related to this:
> Instead of stopping there, due to infinite growth requirements, we move past the optimal state to excessive automation.
Stopping at the optimal state is only possible if you know what the optimal point is in advance (or maybe if you know that there are not local optimal points that are not global).
If you are familiar with deep learning algorithms, they are based on optimization via gradient descent. You are taking steps of a fixed size in the direction that you think is optimal, and by doing so, you are always going to move past the optimal point. The good thing is that the algorithm has a way of evaluating if that is the case, and then moving back in the next step. That moving back is similar to what you are proposing when you say:
> Optimal automation is intimately related to degrowth.
Thanks for sharing! Very interesting point of view. My only comment is related to this:
> Instead of stopping there, due to infinite growth requirements, we move past the optimal state to excessive automation.
Stopping at the optimal state is only possible if you know what the optimal point is in advance (or maybe if you know that there are not local optimal points that are not global).
If you are familiar with deep learning algorithms, they are based on optimization via gradient descent. You are taking steps of a fixed size in the direction that you think is optimal, and by doing so, you are always going to move past the optimal point. The good thing is that the algorithm has a way of evaluating if that is the case, and then moving back in the next step. That moving back is similar to what you are proposing when you say:
> Optimal automation is intimately related to degrowth.